Data Engineer Coach

FIND | Creating Futures
Sheffield
5 months ago
Applications closed

Related Jobs

View all jobs

Scientific Data Engineer - EMEA

Data Engineer

Hybrid Data Engineer — Pipelines, BI & Big Data

Data Engineer

Data Engineer I - QuantumBlack, AI by McKinsey

Data Engineer

Data Engineering Coach - Remote working


Are you a technically strong Data Engineer who loves mentoring others?

Do you want to help shape the next generation of Data Engineers

On behalf of a scaling Technology Training provider, we’re looking for a Data Engineering Coach to train and coach Data Engineering apprentices


You’ll use your technical expertise to deliver training, provide hands-on coaching, and help learners build the real-world skills and confidence they need to succeed in their careers.

What you’ll be doing:

  • Coaching & training Data Engineering apprentices on 12-week dedicated Data Engineering training programmes.
  • Deliver hands-on, interactive group training to the apprentices, from foundation to more advanced Data Engineering topics & practices
  • Provide further 1-to-1 & small group coaching to the apprentices
  • Act as the SME for the 12-week Data Engineering programme, ensuring it’s up to date with all of the latest tools, trends & practices within Data Engineering.
  • Running interactive workshops, kick-off sessions, and wrap-ups/assessment, while guiding learners through self-led content.
  • Being client facing – discussing technical skills gaps & training requirements with this companies client base.


About you:

We’re NOT looking for an Academic – we’re looking for a technically solid Data Engineer who enjoys coaching & mentoring others, and knowledge sharing.

You’ll bring:

  • Strong industry experience within Data Engineering & Cloud
  • Experience with these tools, or familiar: Python, SQL, Kafka, Airflow, Azure, AWS
  • DevOps experience would be a plus (desirable)
  • Formal OR informal Training or Coaching experience (Coach / Mentor / Trainer / Instructor / Tutor / Lecturer etc.)
  • Resilience and openness – comfortable in a fast-moving, start-up or scale-up style environment


This is a remote working position, meaning you can be based from anywhere within the UK.

Salary is £65,000 (negotiable)


Please reach out for more info:

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.